Papers with Code 아카이브
Semantic Parsing
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Semantic Parsing is the task of transducing natural language utterances into formal meaning representations. The target meaning representations can be defined according to a wide variety of formalisms. This include linguistically-motivated semantic representations that are designed to capture the meaning of any sentence such as λ-calculus or the abstract meaning representations. Alternatively, for more task-driven approaches to Semantic Parsing, it is common for meaning representations to represent executable programs such as SQL queries, robotic commands, smart phone instructions, and even general-purpose programming languages like Python and Java.
Source: Tranx: A Transition-based Neural Abstract Syntax Parser for Semantic Parsing and Code Generation
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/wangpeiyi9979/hcl-text2amr
공식 코드https://github.com/wangpinggl/TREQS
공식 코드Text-to-SQL Generation for Question Answering on Electronic Medical Records
https://github.com/wangxinyu0922/Second_Order_SDP
공식 코드Second-Order Semantic Dependency Parsing with End-to-End Neural Networks
https://github.com/wanmok/metametric
공식 코드A Unified View of Evaluation Metrics for Structured Prediction
https://github.com/wasiahmad/Syntax-MBERT
공식 코드Syntax-augmented Multilingual BERT for Cross-lingual Transfer
https://github.com/wbbeyourself/dte
공식 코드Know What I don't Know: Handling Ambiguous and Unanswerable Questions for Text-to-SQL
https://github.com/wbbeyourself/mac-sql
공식 코드MAC-SQL: A Multi-Agent Collaborative Framework for Text-to-SQL
https://github.com/wbsg-uni-mannheim/selfrefinement4extractgpt
공식 코드Automated Self-Refinement and Self-Correction for LLM-based Product Attribute Value Extraction
https://github.com/wotchin/AnDB
공식 코드AnDB: Breaking Boundaries with an AI-Native Database for Universal Semantic Analysis
https://github.com/x-lance/medical-dataset
공식 코드CSS: A Large-scale Cross-schema Chinese Text-to-SQL Medical Dataset
https://github.com/x-lance/text2sql-gpt
공식 코드ACT-SQL: In-Context Learning for Text-to-SQL with Automatically-Generated Chain-of-Thought
https://github.com/x-lance/text2sql-multiturn-gpt
공식 코드CoE-SQL: In-Context Learning for Multi-Turn Text-to-SQL with Chain-of-Editions
https://github.com/xdqkid/S2S-AMR-Parser
공식 코드Improving AMR Parsing with Sequence-to-Sequence Pre-training
https://github.com/xgenerationlab/xiyan-dbdescgen
공식 코드https://github.com/xinyu1205/Recognize_Anything-Tag2Text
공식 코드https://github.com/xiulinyang/compositional_drs_parsing
공식 코드https://github.com/yakazimir/Code-Datasets
공식 코드https://github.com/yale-lily/reastap
공식 코드ReasTAP: Injecting Table Reasoning Skills During Pre-training via Synthetic Reasoning Examples
https://github.com/yandex-research/sparqling-queries
공식 코드SPARQLing Database Queries from Intermediate Question Decompositions
https://github.com/yasufumy/spider-schema-linking-dataset
공식 코드An Investigation Between Schema Linking and Text-to-SQL Performance
https://github.com/ygan/natsql
공식 코드Natural SQL: Making SQL Easier to Infer from Natural Language Specifications
https://github.com/ygan/spider-dk
공식 코드Exploring Underexplored Limitations of Cross-Domain Text-to-SQL Generalization
https://github.com/ygan/spiderss-spidercg
공식 코드Measuring and Improving Compositional Generalization in Text-to-SQL via Component Alignment
https://github.com/yhshu/distribution-shifts-for-kbqa
공식 코드Data Distribution Bottlenecks in Grounding Language Models to Knowledge Bases
https://github.com/yizhang-unifr/xmode
공식 코드Explainable Multi-Modal Data Exploration in Natural Language via LLM Agent
https://github.com/ynklab/SGET
공식 코드Evaluating Structural Generalization in Neural Machine Translation
https://github.com/ynklab/character_order_analysis
공식 코드Does Character-level Information Always Improve DRS-based Semantic Parsing?
https://github.com/ypapanik/t5-for-code-generation
공식 코드Teach me how to Label: Labeling Functions from Natural Language with Text-to-text Transformers
https://github.com/ysu1989/CrossSemparse
공식 코드https://github.com/yuchen2199/explainable-driver-attention-prediction
공식 코드Where, What, Why: Towards Explainable Driver Attention Prediction
https://github.com/yunhaoyang234/Multi-Filter-Seq2Seq-Model
공식 코드https://github.com/yushi-hu/ic-dst
공식 코드https://github.com/yxw-11/tablap
공식 코드Accurate and Regret-aware Numerical Problem Solver for Tabular Question Answering
https://github.com/zechagl/NLPCC2019-Semantic-Parsing
공식 코드https://github.com/zhanzecheng/IRNet
공식 코드Towards Complex Text-to-SQL in Cross-Domain Database with Intermediate Representation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.